{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import *\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "IMAGE_DIR = 'image_contest_level_2_validate'\n",
    "CROP_DIR = 'crop_split_test'\n",
    "\n",
    "from multiprocessing import Pool, Lock, Manager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据并行预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def f(index):\n",
    "    img = cv2.imread('%s/%d.png'%(IMAGE_DIR, index))\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    eq = cv2.equalizeHist(gray)\n",
    "    b = cv2.medianBlur(eq, 9)\n",
    "    \n",
    "    m, n = img.shape[:2]\n",
    "    b2 = cv2.resize(b, (n//4, m//4))\n",
    "\n",
    "    m1 = cv2.morphologyEx(b2, cv2.MORPH_OPEN, np.ones((7, 40)))\n",
    "    m2 = cv2.morphologyEx(m1, cv2.MORPH_CLOSE, np.ones((4, 4)))\n",
    "    _, bw = cv2.threshold(m2, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "    \n",
    "    bw = cv2.resize(bw, (n, m))\n",
    "\n",
    "    img2, ctrs, hier = cv2.findContours(bw, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    if len(ctrs) > 3:\n",
    "        print index\n",
    "    \n",
    "    # 微调三个公式\n",
    "    d = 10\n",
    "    imgs = []\n",
    "    sizes = []\n",
    "    for i, ctr in enumerate(ctrs):\n",
    "        x, y, w, h = cv2.boundingRect(ctr)\n",
    "        if w*h < 1000:\n",
    "            continue\n",
    "        roi = img[max(0, y-d):min(m, y+h+d),max(0, x-d):min(n, x+w+d)]\n",
    "        \n",
    "        x = b[max(0, y-d):min(m, y+h+d),max(0, x-d):min(n, x+w+d)]\n",
    "        _, x = cv2.threshold(x, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "        _, x, _ = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "        x, y, w, h = cv2.boundingRect(np.vstack(x))\n",
    "        roi2 = roi[y:y+h,x:x+w]\n",
    "        imgs.append(roi2)\n",
    "        cv2.imwrite('%s/%d_%d.png'%(CROP_DIR, index, i), roi2)\n",
    "        sizes.append((w, h))\n",
    "    \n",
    "    # 连接三个公式\n",
    "    sizes = np.array(sizes)\n",
    "    w, h = sizes[:,1].max(), sizes[:,0].sum()+(len(sizes)-1)*2\n",
    "    img = np.zeros((w, h, 3), dtype=np.uint8)\n",
    "    x = 0\n",
    "    for a in imgs[::-1]:\n",
    "        iw = a.shape[1]\n",
    "        img[:a.shape[0], x:x+iw] = a\n",
    "        x += iw + 2\n",
    "    \n",
    "    cv2.imwrite('%s/%d.png'%(CROP_DIR, index), img)\n",
    "    \n",
    "    return [index, w, h, m, n, len(sizes)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100000/100000 [23:46<00:00, 70.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 21.4 s, sys: 3.34 s, total: 24.8 s\n",
      "Wall time: 23min 46s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "try:\n",
    "    p\n",
    "except:\n",
    "    p = Pool(12)\n",
    "\n",
    "n = 100000\n",
    "if __name__ == '__main__':\n",
    "    rs = []\n",
    "    for r in tqdm(p.imap_unordered(f, range(n)), total=n):\n",
    "        rs.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w</th>\n",
       "      <th>h</th>\n",
       "      <th>m</th>\n",
       "      <th>n</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>780</td>\n",
       "      <td>500</td>\n",
       "      <td>3200</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59</td>\n",
       "      <td>731</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>715</td>\n",
       "      <td>400</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>763</td>\n",
       "      <td>1000</td>\n",
       "      <td>4000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>769</td>\n",
       "      <td>600</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    w    h     m     n  r\n",
       "i                        \n",
       "0  59  780   500  3200  3\n",
       "1  59  731  1000  4000  2\n",
       "2  60  715   400  2400  3\n",
       "3  48  763  1000  4000  3\n",
       "4  65  769   600  2400  3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(rs, columns=list('iwhmnr'))\n",
    "df.index = df['i']\n",
    "df = df.sort_index()\n",
    "df = df.drop('i', 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w</th>\n",
       "      <th>h</th>\n",
       "      <th>m</th>\n",
       "      <th>n</th>\n",
       "      <th>r</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [w, h, m, n, r]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['r'] > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('r_test.csv', index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 结果可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def disp2(img):\n",
    "    cv2.imwrite('a.png', img)\n",
    "    return Image('a.png')\n",
    "\n",
    "\n",
    "def disp(img, txt=None, first=False):\n",
    "    global index\n",
    "    if first:\n",
    "        index = 1\n",
    "        plt.figure(figsize=(16, 9))\n",
    "    else:\n",
    "        index += 1\n",
    "    plt.subplot(2, 3, index)\n",
    "    if len(img.shape) == 2:\n",
    "        plt.imshow(img, cmap='gray')\n",
    "    else:\n",
    "        plt.imshow(img[:,:,::-1])\n",
    "    if txt:\n",
    "        plt.title(txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 技术原理\n",
    "\n",
    "* [转灰度图](http://docs.opencv.org/master/df/d9d/tutorial_py_colorspaces.html)\n",
    "* [二值化](http://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html)\n",
    "* [直方图均衡](http://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html)\n",
    "* [中值滤波](http://docs.opencv.org/master/d4/d13/tutorial_py_filtering.html)\n",
    "* [开运算](http://docs.opencv.org/master/d9/d61/tutorial_py_morphological_ops.html)\n",
    "* [轮廓查找](http://docs.opencv.org/master/d4/d73/tutorial_py_contours_begin.html)\n",
    "* [边界矩形](http://docs.opencv.org/master/dd/d49/tutorial_py_contour_features.html)\n",
    "\n",
    "参考链接：\n",
    "\n",
    "* http://docs.opencv.org/master/df/d9d/tutorial_py_colorspaces.html\n",
    "* http://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html\n",
    "* http://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html\n",
    "* http://docs.opencv.org/master/d4/d13/tutorial_py_filtering.html\n",
    "* http://docs.opencv.org/master/d9/d61/tutorial_py_morphological_ops.html\n",
    "* http://docs.opencv.org/master/d4/d73/tutorial_py_contours_begin.html\n",
    "* http://docs.opencv.org/master/dd/d49/tutorial_py_contour_features.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArQAAAAqCAIAAADNr1+XAAAZf0lEQVR4Ae3BMYzk7YGQ+efpuYSe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\nV1999dWX538DLINGVHmpbgUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABwYAAAQvCAYAAAAdG4omAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f293cf1bad0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 535,
       "width": 899
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(index):\n",
    "    global img, gray, b, eq, bw, m1, m2, r, roi\n",
    "    img = cv2.imread('%s/%d.png'%(IMAGE_DIR, index))\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    eq = cv2.equalizeHist(gray)\n",
    "    b = cv2.medianBlur(eq, 9)\n",
    "    \n",
    "    m, n = img.shape[:2]\n",
    "    b2 = cv2.resize(b, (n//4, m//4))\n",
    "\n",
    "    m1 = cv2.morphologyEx(b2, cv2.MORPH_OPEN, np.ones((7, 40)))\n",
    "    m2 = cv2.morphologyEx(m1, cv2.MORPH_CLOSE, np.ones((4, 4)))\n",
    "    _, bw = cv2.threshold(m2, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "    \n",
    "    bw = cv2.resize(bw, (n, m))\n",
    "\n",
    "    r = img.copy()\n",
    "    img2, ctrs, hier = cv2.findContours(bw, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    for ctr in ctrs:\n",
    "        x, y, w, h = cv2.boundingRect(ctr)\n",
    "        cv2.rectangle(r, (x, y), (x+w, y+h), (0, 255, 0), 10)\n",
    "    x, y, w, h = cv2.boundingRect(np.vstack(ctrs))\n",
    "    \n",
    "    disp(img, 'raw img', 1)\n",
    "    disp(eq, 'eq')\n",
    "    disp(b, 'blur')\n",
    "    disp(m1, 'm1')\n",
    "    disp(m2, 'm2')\n",
    "    disp(r, 'rect')\n",
    "    \n",
    "    # 微调三个公式\n",
    "    d = 10\n",
    "    imgs = []\n",
    "    sizes = []\n",
    "    for i, ctr in enumerate(ctrs):\n",
    "        x, y, w, h = cv2.boundingRect(ctr)\n",
    "        roi = img[max(0, y-d):min(m, y+h+d),max(0, x-d):min(n, x+w+d)]\n",
    "        \n",
    "        x = b[max(0, y-d):min(m, y+h+d),max(0, x-d):min(n, x+w+d)]\n",
    "        _, x = cv2.threshold(x, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "        _, x, _ = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "        x, y, w, h = cv2.boundingRect(np.vstack(x))\n",
    "        roi2 = roi[y:y+h,x:x+w]\n",
    "        imgs.append(roi2)\n",
    "        sizes.append((w, h))\n",
    "    \n",
    "    # 连接三个公式\n",
    "    sizes = np.array(sizes)\n",
    "    img = np.zeros((sizes[:,1].max(), sizes[:,0].sum()+len(sizes)-1, 3), dtype=np.uint8)\n",
    "    x = 0\n",
    "    for a in imgs[::-1]:\n",
    "        w = a.shape[1]\n",
    "        img[:a.shape[0], x:x+w] = a\n",
    "        x += w + 1\n",
    "    \n",
    "    return disp2(img)\n",
    "    \n",
    "plot(95170)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
